show data

head(wildschwein_BE)
## # A tibble: 6 x 8
##   TierID TierName CollarID DatetimeUTC                E        N day            
##    <int> <chr>       <int> <dttm>                 <dbl>    <dbl> <chr>          
## 1      1 Ueli        12272 2014-05-28 21:01:14 2570390. 1204820. Tag            
## 2      1 Ueli        12272 2014-05-28 21:15:18 2570389. 1204826. Abenddaemmerung
## 3      1 Ueli        12272 2014-05-28 21:30:13 2570391. 1204821. Abenddaemmerung
## 4      1 Ueli        12272 2014-05-28 21:45:11 2570388. 1204826. Abenddaemmerung
## 5      1 Ueli        12272 2014-05-28 22:00:33 2570388. 1204819. 1Nachtviertel  
## 6      1 Ueli        12272 2014-05-28 22:15:16 2570384. 1204828. 1Nachtviertel  
## # … with 1 more variable: moonilumination <dbl>
head(wildschwein_metadata)
##   TierID TierName CollarID Sex Gewicht Study_area
## 1      1     Ueli    12272   m    79.5       Bern
## 2      1     Ueli    12844   m    91.0       Bern
## 3      2   Sabine    12275   f    62.0       Bern
## 4      5   Nicole    12273   f    50.0       Bern
## 5     10 Caroline    13570   f    68.0       Bern
## 6     10 Caroline    13969   f    58.0       Bern
head(wildschwein_overlap_temp)
## # A tibble: 6 x 4
##   TierID TierName CollarID Groups
##    <int> <chr>       <int>  <dbl>
## 1      1 Ueli        12272      1
## 2      2 Sabine      12275      2
## 3      5 Nicole      12273      2
## 4     10 Caroline    13969      2
## 5     11 Isabelle    12274      2
## 6     16 Rosa        13972      2
head(schreck_agenda)
## # A tibble: 6 x 9
##   id       datum_on            datum_off           modus   lautstaerke intervall
##   <chr>    <dttm>              <dttm>              <chr>         <dbl>     <dbl>
## 1 WSS_201… 2014-04-01 00:00:00 2014-06-20 00:00:00 standa…         100        15
## 2 WSS_201… 2014-07-23 00:00:00 2014-09-19 00:00:00 standa…         100        15
## 3 WSS_201… 2014-04-26 00:00:00 2014-08-08 00:00:00 standa…          50         4
## 4 WSS_201… 2014-04-26 00:00:00 2014-08-08 00:00:00 standa…         100        15
## 5 WSS_201… 2014-09-19 00:00:00 2014-10-18 00:00:00 standa…          50        20
## 6 WSS_201… 2014-05-01 00:00:00 2014-10-28 00:00:00 standa…          33        15
## # … with 3 more variables: ausrichtung_min <int>, ausrichtung_max <int>,
## #   phase <dbl>
head(schreck_locations)
## # A tibble: 6 x 9
##   id     region  flurname kultur    installationsho… zaun  jagddruck   lat   lon
##   <chr>  <chr>   <chr>    <chr>                <dbl> <chr> <chr>     <dbl> <dbl>
## 1 WSS_2… elfing… rüti     weizen&h…             1.8  nein  mittel     47.5  8.10
## 2 WSS_2… elfing… steiach… weizen                1.95 nein  mittel     47.5  8.12
## 3 WSS_2… elfing… schlott… weizen&s…             1.8  nein  mittel     47.5  8.11
## 4 WSS_2… fanel   tannenh… kartoffe…             1.8  ja    gering     47.0  7.06
## 5 WSS_2… fanel   tannenh… karotten…             1.8  nein  gering     47.0  7.06
## 6 WSS_2… fanel   fanelac… kartoffe…             1.8  nein  gering     47.0  7.04

Schreck prepare

schreck_locations_ch <- schreck_locations %>% st_as_sf(coords = c("lon", "lat"), crs = CRS("+init=epsg:4326"), remove = FALSE)  #%>% st_transform(crs = 2056)
schreck_locations_ch <- schreck_locations_ch %>% st_transform(crs = 2056)
schreck_locations_ch <- schreck_locations_ch %>% filter(lat < 47.2 & lon < 7.5)
coordsne <- unlist(st_geometry(schreck_locations_ch)) %>% matrix(ncol=2,byrow=TRUE) %>% as_tibble() %>% setNames(c("N","E"))
## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
schreck_locations_ch$N <- coordsne$E
schreck_locations_ch$E <- coordsne$N
#join 
schreck_locations_ch <- schreck_locations_ch %>% left_join(schreck_agenda, by=c("id"="id"))
schreck_locations_ch$wid <- c(1:25)
schreck_locations_ch <- schreck_locations_ch %>% mutate(wid=as.character(wid))

Get common data, show examples

# Get common samples
head(wildschwein_BE)
## # A tibble: 6 x 8
##   TierID TierName CollarID DatetimeUTC                E        N day            
##    <int> <chr>       <int> <dttm>                 <dbl>    <dbl> <chr>          
## 1      1 Ueli        12272 2014-05-28 21:01:14 2570390. 1204820. Tag            
## 2      1 Ueli        12272 2014-05-28 21:15:18 2570389. 1204826. Abenddaemmerung
## 3      1 Ueli        12272 2014-05-28 21:30:13 2570391. 1204821. Abenddaemmerung
## 4      1 Ueli        12272 2014-05-28 21:45:11 2570388. 1204826. Abenddaemmerung
## 5      1 Ueli        12272 2014-05-28 22:00:33 2570388. 1204819. 1Nachtviertel  
## 6      1 Ueli        12272 2014-05-28 22:15:16 2570384. 1204828. 1Nachtviertel  
## # … with 1 more variable: moonilumination <dbl>
head(schreck_locations_ch)
## Simple feature collection with 6 features and 20 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 2569629 ymin: 1204878 xmax: 2571106 ymax: 1207100
## Projected CRS: CH1903+ / LV95
## # A tibble: 6 x 21
##   id     region flurname  kultur    installationsho… zaun  jagddruck   lat   lon
##   <chr>  <chr>  <chr>     <chr>                <dbl> <chr> <chr>     <dbl> <dbl>
## 1 WSS_2… fanel  tannenhof kartoffe…              1.8 ja    gering     47.0  7.06
## 2 WSS_2… fanel  tannenhof karotten…              1.8 nein  gering     47.0  7.06
## 3 WSS_2… fanel  fanelach… kartoffe…              1.8 nein  gering     47.0  7.04
## 4 WSS_2… fanel  fanelach… kartoffe…              1.8 nein  gering     47.0  7.04
## 5 WSS_2… fanel  tannenhof weizen                 1.8 nein  gering     47.0  7.06
## 6 WSS_2… fanel  tannenhof weizen                 1.8 nein  gering     47.0  7.06
## # … with 12 more variables: geometry <POINT [m]>, N <dbl>, E <dbl>,
## #   datum_on <dttm>, datum_off <dttm>, modus <chr>, lautstaerke <dbl>,
## #   intervall <dbl>, ausrichtung_min <int>, ausrichtung_max <int>, phase <dbl>,
## #   wid <chr>
sabi <- wildschwein_BE %>% filter(TierName=="Sabine")
# Filter night data
sabi <- sabi %>% filter(day != "Tag" & !is.na(day)) # only night gps

s1 <- schreck_locations_ch[5,]
sabi <- sabi %>% filter(DatetimeUTC > first(s1$datum_on) & DatetimeUTC < first(s1$datum_off))
sabi <- sabi %>% mutate(dist = sqrt((first(s1$N)-N)^2+(first(s1$E)-E)^2))
sabi <- sabi %>% filter(dist < 400)
sabi <- sabi %>% mutate(trip = ifelse(hour(DatetimeUTC) > 16, day(DatetimeUTC)+1, day(DatetimeUTC)))


ggplot() +
  geom_path(data=sabi %>%  filter(trip<11), aes(x=E, y=N, color=factor(trip))) +
  geom_point(data = s1, aes(x=E, y=N), colour="black", size=2) 

# Get example 1
wildboar_closeup <- wildschwein_BE %>% 
  filter(TierName=="Sabine" & DatetimeUTC > as_datetime('2015-06-09 22:30:43') & DatetimeUTC < as_datetime('2015-06-10 14:30:43')) %>% 
  mutate(dist = sqrt((first(s1$N)-N)^2+(first(s1$E)-E)^2)) %>% mutate(triptime0 = as.numeric((DatetimeUTC - min(DatetimeUTC))) / 60)
ggplot() +
  geom_path(data=wildboar_closeup, aes(x=E, y=N, color=triptime0)) +
  geom_point(data = s1, aes(x=E, y=N), colour="black", size=2) 

# Get example 2
wildboar_closeup1 <- wildschwein_BE %>% 
  filter(TierName=="Sabine" & DatetimeUTC > as_datetime('2015-06-08 18:30:43') & DatetimeUTC < as_datetime('2015-06-09 14:30:43')) %>% 
  mutate(dist = sqrt((first(s1$N)-N)^2+(first(s1$E)-E)^2)) %>% mutate(triptime0 = as.numeric((DatetimeUTC - min(DatetimeUTC))) / 60) %>% arrange(.,triptime0)
ggplot() +
  geom_path(data=wildboar_closeup1, aes(x=E, y=N, color=triptime0)) +
  geom_point(data = s1, aes(x=E, y=N), colour="black", size=2) 

# Get example 2
wildboar_closeup2 <- wildschwein_BE %>% 
  filter(TierName=="Sabine" & DatetimeUTC > as_datetime('2015-06-07 18:30:43') & DatetimeUTC < as_datetime('2015-06-08 14:30:43')) %>% 
  mutate(dist = sqrt((first(s1$N)-N)^2+(first(s1$E)-E)^2)) %>% mutate(triptime0 = as.numeric((DatetimeUTC - min(DatetimeUTC))) / 60) %>% arrange(.,triptime0)
ggplot() +
  geom_path(data=wildboar_closeup2, aes(x=E, y=N, color=triptime0)) +
  geom_point(data = s1, aes(x=E, y=N), colour="black", size=2) 

# Plot
head(schreck_locations_ch)
## Simple feature collection with 6 features and 20 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 2569629 ymin: 1204878 xmax: 2571106 ymax: 1207100
## Projected CRS: CH1903+ / LV95
## # A tibble: 6 x 21
##   id     region flurname  kultur    installationsho… zaun  jagddruck   lat   lon
##   <chr>  <chr>  <chr>     <chr>                <dbl> <chr> <chr>     <dbl> <dbl>
## 1 WSS_2… fanel  tannenhof kartoffe…              1.8 ja    gering     47.0  7.06
## 2 WSS_2… fanel  tannenhof karotten…              1.8 nein  gering     47.0  7.06
## 3 WSS_2… fanel  fanelach… kartoffe…              1.8 nein  gering     47.0  7.04
## 4 WSS_2… fanel  fanelach… kartoffe…              1.8 nein  gering     47.0  7.04
## 5 WSS_2… fanel  tannenhof weizen                 1.8 nein  gering     47.0  7.06
## 6 WSS_2… fanel  tannenhof weizen                 1.8 nein  gering     47.0  7.06
## # … with 12 more variables: geometry <POINT [m]>, N <dbl>, E <dbl>,
## #   datum_on <dttm>, datum_off <dttm>, modus <chr>, lautstaerke <dbl>,
## #   intervall <dbl>, ausrichtung_min <int>, ausrichtung_max <int>, phase <dbl>,
## #   wid <chr>
ggplot() +
  geom_sf(data = schreck_locations_ch, color='black') +
  geom_sf(data=wildschwein_BE %>% filter(TierName=="Sabi"), color="blue")

ggplot() +
  geom_point(data = schreck_locations_ch, aes(x=E, y=N, color='red')) +
  geom_point(data=sabi, aes(x=E, y=N,color="blue")) + ylim(1200000, 1210000) +  xlim(2568000, 2578000)
## Warning: Removed 4 rows containing missing values (geom_point).

sabi %>% filter(day==10)
## # A tibble: 0 x 10
## # … with 10 variables: TierID <int>, TierName <chr>, CollarID <int>,
## #   DatetimeUTC <dttm>, E <dbl>, N <dbl>, day <chr>, moonilumination <dbl>,
## #   dist <dbl>, trip <dbl>

Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.

Adding closest active schreck (+ distance) to each observation

### data
w<-wildschwein_BE %>% mutate(date = as.Date(DatetimeUTC), 
                             time= format(DatetimeUTC, format = "%H:%M:%S"),
                             hour= as.integer(format(DatetimeUTC, format = "%H"))+
                               as.integer(format(DatetimeUTC, format = "%M"))/60)
sl<-schreck_locations

###
s<-data.frame(schreck_locations_ch) ## I had to transform it to a data frame, otherwise something was weird about the coordinates (geometry)
###

s1<-s[!duplicated(s$id),c("id","N","E")] # list with all Schrecks and their location

# Set id to w
w <- w %>% mutate(id = row_number())

## reduce data to certain time frame
w1<-w %>% 
  filter (day!="Tag"&!is.na(day) &
            ((DatetimeUTC > as.Date("2014-05-01") & DatetimeUTC < as.Date("2014-07-04")) | 
                (DatetimeUTC > as.Date("2015-05-20") & DatetimeUTC < as.Date("2015-07-01")) |
                 (DatetimeUTC > as.Date("2016-04-04") & DatetimeUTC < as.Date("2016-10-04")) |
                 (DatetimeUTC > as.Date("2017-04-26") & DatetimeUTC < as.Date("2017-11-18"))))
nrow(w1)
## [1] 38039
#for(j in 1:nrow(w1)){
### look only at Schrecks that were active on that day
 # s_on<-s[s$datum_on < w1[j,]$DatetimeUTC & s$datum_off > w1[j,]$DatetimeUTC,]
  #if(nrow(s_on)==0)
   # {w1[j,"closest_schreck"]<-"no_Schreck_on"}  
  #else{     
    #for(i in 1:nrow(s_on)){ ## calculate difference between current observation (j) and each schreck location
      #s_on[i,"diff"]<-sqrt((w1[j,"N"]-s_on[i,"N"])^2+(w1[j,"E"]-s_on[i,"E"])^2)} ## add difference of current observation to location into file
#### look at the distance of the closest Schreck, only use it if less than 400m
  #if(min(s_on$diff)>400){
      #w1[j,"closest_schreck"]<-"no_Schreck_witin_400m"} 
  #else{
      #w1[j,"closest_schreck"]<-s_on[s_on$diff==min(s_on$diff),"id"]  ## add closest schreck to each wild boar location
      #w1[j,"distance_to_closest_schreck"]<-s_on[s_on$diff==min(s_on$diff),"diff"]
#}}}

# Save data.frame to spare time
#write.csv(w1, "wildboar_loop.csv")

w1 <- read_delim("wildboar_loop.csv",",")
## Warning: Missing column names filled in: 'X1' [1]
## 
## ── Column specification ────────────────────────────────────────────────────────
## cols(
##   X1 = col_double(),
##   TierID = col_double(),
##   TierName = col_character(),
##   CollarID = col_double(),
##   DatetimeUTC = col_datetime(format = ""),
##   E = col_double(),
##   N = col_double(),
##   day = col_character(),
##   moonilumination = col_double(),
##   id = col_double(),
##   closest_schreck = col_character(),
##   distance_to_closest_schreck = col_double()
## )
w1 <- w1 %>% mutate(hour= as.integer(format(DatetimeUTC, format = "%H")),
                tripdate= ifelse(hour < 12, as.Date(DatetimeUTC)-1, as.Date(DatetimeUTC)),
                tripdate2 = as.Date(tripdate, origin="1970-01-01"))

# Merge close wildschweinschreck gps  data to origin wildboar
w <- w %>% left_join(w1 %>% dplyr::select(id, closest_schreck, distance_to_closest_schreck), by="id")
w <- w %>% mutate(closest_schreck= 
  ifelse(is.na(closest_schreck), "no_Schreck_witin_400m", closest_schreck))

#add tripdate
w <- w %>% mutate(hour= as.integer(format(DatetimeUTC, format = "%H")),
                tripdate= ifelse(hour < 12, as.Date(DatetimeUTC)-1, as.Date(DatetimeUTC)),
                tripdate2 = as.Date(tripdate, origin="1970-01-01"))


w %>% dplyr::select(DatetimeUTC, tripdate2)
## # A tibble: 327,255 x 2
##    DatetimeUTC         tripdate2 
##    <dttm>              <date>    
##  1 2014-05-28 21:01:14 2014-05-28
##  2 2014-05-28 21:15:18 2014-05-28
##  3 2014-05-28 21:30:13 2014-05-28
##  4 2014-05-28 21:45:11 2014-05-28
##  5 2014-05-28 22:00:33 2014-05-28
##  6 2014-05-28 22:15:16 2014-05-28
##  7 2014-05-28 22:30:14 2014-05-28
##  8 2014-05-28 22:45:09 2014-05-28
##  9 2014-05-28 23:00:12 2014-05-28
## 10 2014-05-28 23:15:08 2014-05-28
## # … with 327,245 more rows

Separation of the wildboar trips

# split time and day in separate columns:
w1$Date <- as.Date(w1$DatetimeUTC)
## order data frame by animal and time
w1<-w1[order(w1$TierName) & order(w1$DatetimeUTC),]


##### Create trips: with shorest distance to schreck (for each tripdate) ########################################

# split time and day in separate columns:
w1$Date <- as.Date(w1$DatetimeUTC)

## order data frame by animal and time
w1<-w1[order(w1$TierName) & order(w1$DatetimeUTC),]

## empty column for trip id
w$tripID_dist<-"NA"
w$start_dist<-"no"
w$isNearestPoint <- "no"
animals<-unique(w1$TierName)

for(j in 1:length(animals)){
  days<-unique(w1[w1$TierName==animals[j] & !is.na(w1$TierName),]$tripdate2) 
  if(length(days)!=0){
    for(i in 1:length(days)){
      n<-w1[w1$TierName==animals[j] & w1$tripdate2==days[i],]
      if(nrow(n[!is.na(n$distance_to_closest_schreck),])==0){nmin<-NA} else{
      nmin<-min(n$distance_to_closest_schreck,na.rm=T)}
      if(!is.na(nmin)){ ## only continue if nmin is not NA
        if(nmin<=400){ ## only continue if minimal distance to schreck is less than 400m
      n<-n[n$distance_to_closest_schreck==nmin & !is.na(n$distance_to_closest_schreck),]
      w[w$TierName==animals[j] & w$DatetimeUTC == n$DatetimeUTC, "isNearestPoint"] <- "yes"
        w[w$TierName==animals[j] & (w$DatetimeUTC >= n$DatetimeUTC-4*60*60) & (w$DatetimeUTC <= n$DatetimeUTC+4*60*60),"tripID_dist"]<-paste(animals[j],i, sep="_")                               
        w[w$TierName==animals[j] & w$DatetimeUTC==n$DatetimeUTC,"start_dist"]<-"yes"
      }}}}}
## Warning in `==.default`(w$DatetimeUTC, n$DatetimeUTC): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes
## Warning in `>=.default`(w$DatetimeUTC, n$DatetimeUTC - 4 * 60 * 60): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes
## Warning in `<=.default`(w$DatetimeUTC, n$DatetimeUTC + 4 * 60 * 60): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes
## Warning in `==.default`(w$DatetimeUTC, n$DatetimeUTC): Länge des längeren Objektes
##       ist kein Vielfaches der Länge des kürzeren Objektes
# Only trips in the night
w <- w %>% group_by(TierID, tripdate2) %>% mutate(isTrip= max(tripID_dist) != 'NA',
                                          tripIDnight = ifelse((hour < 9.1 | hour >= 17.9)&isTrip,
                                                              max(tripID_dist), NA)) %>% ungroup()


# length(unique(w$tripIDnight)); table(w[!is.na(w$tripIDnight),]$isNearestPoint)

#List with closest_distance to schreck for each trip
trip_list <- w %>% 
  filter (isNearestPoint=="yes" & !is.na (tripIDnight))

nrow(trip_list)
## [1] 113
head(data.frame(trip_list))
##   TierID TierName CollarID         DatetimeUTC       E       N           day
## 1      1     Ueli    12272 2014-05-29 01:00:29 2570591 1205129 2Nachtviertel
## 2      1     Ueli    12272 2014-05-30 01:00:15 2570746 1204849 2Nachtviertel
## 3      1     Ueli    12272 2014-05-31 02:45:12 2570808 1204667 3Nachtviertel
## 4      1     Ueli    12272 2014-06-01 02:30:08 2570881 1204652 3Nachtviertel
## 5      1     Ueli    12272 2014-06-02 02:30:09 2570976 1204687 3Nachtviertel
## 6      1     Ueli    12272 2014-06-03 01:30:11 2570976 1204687 3Nachtviertel
##   moonilumination       date     time hour  id closest_schreck
## 1     0.002343097 2014-05-29 01:00:29    1  17     WSS_2014_04
## 2     0.023774932 2014-05-30 01:00:15    1  69     WSS_2014_05
## 3     0.066684515 2014-05-31 02:45:12    2 128     WSS_2014_05
## 4     0.129136625 2014-06-01 02:30:08    2 179     WSS_2014_05
## 5     0.208314672 2014-06-02 02:30:09    2 231     WSS_2014_05
## 6     0.300647726 2014-06-03 01:30:11    1 279     WSS_2014_05
##   distance_to_closest_schreck tripdate  tripdate2 tripID_dist start_dist
## 1                    350.7307    16218 2014-05-28      Ueli_1        yes
## 2                    361.1836    16219 2014-05-29      Ueli_2        yes
## 3                    364.5964    16220 2014-05-30      Ueli_3        yes
## 4                    318.3504    16221 2014-05-31      Ueli_4        yes
## 5                    230.2212    16222 2014-06-01      Ueli_5        yes
## 6                    230.8985    16223 2014-06-02      Ueli_6        yes
##   isNearestPoint isTrip tripIDnight
## 1            yes   TRUE      Ueli_1
## 2            yes   TRUE      Ueli_2
## 3            yes   TRUE      Ueli_3
## 4            yes   TRUE      Ueli_4
## 5            yes   TRUE      Ueli_5
## 6            yes   TRUE      Ueli_6

Overview data

### plot
pa<-ggplot(w,aes(x=date,y=TierName))+geom_line()
pa<-pa+ylab("animal ID")+scale_x_date(breaks = "3 month", minor_breaks = "1 month")+xlab("date")+theme_bw()
pa

#ps<-ggplot(s[s$id %in% w$closest_schreck,],aes(y=id,yend=id,x=datum_on,xend=datum_off))+geom_segment()
#ps

Wildschewinschreck

# Determine mean speaker orientation
schreck_locations_ch <- schreck_locations_ch %>% 
  mutate(ausrichtung_mean = (ausrichtung_max - ausrichtung_min) / 2 +ausrichtung_min,
         ausrichtung_dir = ifelse(ausrichtung_mean >= 45 & ausrichtung_mean < 135, "E", 
                                  ifelse(ausrichtung_mean > 135 & ausrichtung_mean < 225, "S",
                                         ifelse(ausrichtung_mean > 225 & ausrichtung_mean < 315, "W", "N")))) 


s1 <- schreck_locations_ch[5,]

wildboar_closeup <- wildschwein_BE %>% 
  filter(TierName=="Sabine" & DatetimeUTC > as_datetime('2015-06-08 18:30:43') & DatetimeUTC < as_datetime('2015-06-09 10:30:43')) %>% 
  mutate(dist = sqrt((first(s1$N)-N)^2+(first(s1$E)-E)^2)) %>% mutate(triptime0 = as.numeric((DatetimeUTC - min(DatetimeUTC))) / 60) %>% arrange(.,triptime0)

schreck_orientation <- s1 %>% mutate(length=lautstaerke*lautstaerke/100)
rad2deg <- function(rad) {(rad * 180) / (pi)}
deg2rad <- function(deg) {(deg * pi) / (180)}

schreck_orientation$asurichtung_mean <- (schreck_orientation$ausrichtung_max-
                                           schreck_orientation$ausrichtung_min) / 2 +
                                        schreck_orientation$ausrichtung_min
schreck_orientation$ausrichung_meanE = schreck_orientation$E[1] + schreck_orientation$length[1] * 2*
  cos(deg2rad(360-270-(schreck_orientation$asurichtung_mean[1])))
schreck_orientation$ausrichung_meanN = schreck_orientation$N[1] + schreck_orientation$length[1] *2* 
  sin(deg2rad(360-270-(schreck_orientation$asurichtung_mean[1])))

schreck_orientation$ausrichung_minE = schreck_orientation$E[1] + schreck_orientation$length[1] * 
  cos(deg2rad(360-270-schreck_orientation$ausrichtung_min[1]))
schreck_orientation$ausrichung_minN = schreck_orientation$N[1] + schreck_orientation$length[1] * 
  sin(deg2rad(360-270-schreck_orientation$ausrichtung_min[1]))
schreck_orientation$ausrichung_maxE = schreck_orientation$E[1] + schreck_orientation$length[1] * 
  cos(deg2rad(360-270-schreck_orientation$ausrichtung_max[1]))
schreck_orientation$ausrichung_maxN = schreck_orientation$N[1] + schreck_orientation$length[1] * 
  sin(deg2rad(360-270-schreck_orientation$ausrichtung_max[1]))


x_coord <- c(schreck_orientation$E[1],  schreck_orientation$ausrichung_minE[1],
             schreck_orientation$ausrichung_meanE[1],
             schreck_orientation$ausrichung_maxE[1], schreck_orientation$E[1])
y_coord <- c(schreck_orientation$N[1], schreck_orientation$ausrichung_minN[1],
             schreck_orientation$ausrichung_meanN[1],
             schreck_orientation$ausrichung_maxN[1], schreck_orientation$N[1])
#p = Polygon(cbind(x_coord, y_coord))
#ps = Polygons(list(p),1)
#sps = SpatialPolygons(list(ps))
#plot(sps)
#schreck_orientation$polygon[1] = sps[1]
poly <- st_polygon(list(matrix(c(x_coord, y_coord),ncol=2, byrow=FALSE))) #%>% st_geometry(poly) %>% st_set_crs(2056)
pos <- data.frame(x=x_coord, y=y_coord, id=c(1,2,3,4,5))
#schreck_orientation$polygon <- poly 
                                                                        

ggplot() +
  geom_path(data=wildboar_closeup, aes(x=E, y=N, color=triptime0)) +
  geom_point(data = s1, aes(x=E, y=N), colour="black", size=2) +
  geom_point(data = schreck_orientation, aes(x=ausrichung_meanE, y=ausrichung_meanN), 
             colour="brown", size=2) +
  geom_polygon(data=pos, aes(x=x, y = y), fill="orange", alpha=0.4)

Wilddboar scare effect properties

# Get all trips
trips <- w %>% filter(!is.na(tripIDnight)) %>% group_by(tripIDnight)
trips 
## # A tibble: 6,661 x 21
## # Groups:   tripIDnight [113]
##    TierID TierName CollarID DatetimeUTC                E        N day           
##     <int> <chr>       <int> <dttm>                 <dbl>    <dbl> <chr>         
##  1      1 Ueli        12272 2014-05-28 21:01:14 2570390. 1204820. Tag           
##  2      1 Ueli        12272 2014-05-28 21:15:18 2570389. 1204826. Abenddaemmeru…
##  3      1 Ueli        12272 2014-05-28 21:30:13 2570391. 1204821. Abenddaemmeru…
##  4      1 Ueli        12272 2014-05-28 21:45:11 2570388. 1204826. Abenddaemmeru…
##  5      1 Ueli        12272 2014-05-28 22:00:33 2570388. 1204819. 1Nachtviertel 
##  6      1 Ueli        12272 2014-05-28 22:15:16 2570384. 1204828. 1Nachtviertel 
##  7      1 Ueli        12272 2014-05-28 22:30:14 2570393. 1204824. 1Nachtviertel 
##  8      1 Ueli        12272 2014-05-28 22:45:09 2570585. 1205044. 1Nachtviertel 
##  9      1 Ueli        12272 2014-05-28 23:00:12 2570576. 1205044. 1Nachtviertel 
## 10      1 Ueli        12272 2014-05-28 23:15:08 2570566. 1205047. 1Nachtviertel 
## # … with 6,651 more rows, and 14 more variables: moonilumination <dbl>,
## #   date <date>, time <chr>, hour <int>, id <dbl>, closest_schreck <chr>,
## #   distance_to_closest_schreck <dbl>, tripdate <dbl>, tripdate2 <date>,
## #   tripID_dist <chr>, start_dist <chr>, isNearestPoint <chr>, isTrip <lgl>,
## #   tripIDnight <chr>
wildboar_trip_scared <- data.frame(matrix(ncol = 22, nrow = 0))
x <- c("id", "TierName","triptime0", "approachingRate", "approachingRateRelative",
       "approachingRateAbsolute", "speed", "sinousity", "linedist", "acceleration", "speedDiff3", 
       "dist","E", "N","x", "y", "directionRelative",
       "closest_schreck", "tripIDnight", "DatetimeUTC", "hour", "day")
colnames(wildboar_trip_scared) <- x
unique(trips$tripIDnight)
##   [1] "Ueli_1"    "Ueli_2"    "Ueli_3"    "Ueli_4"    "Ueli_5"    "Ueli_6"   
##   [7] "Ueli_7"    "Ueli_9"    "Ueli_10"   "Ueli_12"   "Ueli_14"   "Ueli_15"  
##  [13] "Ueli_110"  "Ueli_114"  "Ueli_119"  "Ueli_124"  "Ueli_127"  "Ueli_133" 
##  [19] "Ueli_137"  "Sabine_2"  "Sabine_18" "Sabine_20" "Sabine_21" "Sabine_22"
##  [25] "Sabine_23" "Sabine_24" "Sabine_25" "Sabine_26" "Sabine_27" "Sabine_28"
##  [31] "Sabine_29" "Sabine_30" "Sabine_31" "Sabine_32" "Sabine_33" "Sabine_34"
##  [37] "Sabine_35" "Sabine_36" "Sabine_37" "Sabine_38" "Sabine_39" "Sabine_40"
##  [43] "Sabine_41" "Sabine_42" "Sabine_43" "Ruth_6"    "Ruth_7"    "Ruth_8"   
##  [49] "Ruth_9"    "Ruth_10"   "Ruth_12"   "Ruth_14"   "Ruth_15"   "Ruth_16"  
##  [55] "Ruth_17"   "Ruth_18"   "Ruth_19"   "Ruth_20"   "Ruth_21"   "Ruth_22"  
##  [61] "Ruth_23"   "Ruth_24"   "Ruth_25"   "Ruth_26"   "Ruth_27"   "Ruth_28"  
##  [67] "Ruth_29"   "Ruth_30"   "Ruth_31"   "Ruth_32"   "Ruth_33"   "Ruth_34"  
##  [73] "Ruth_35"   "Ruth_36"   "Ruth_37"   "Ruth_38"   "Ruth_39"   "Ruth_40"  
##  [79] "Ruth_41"   "Ruth_42"   "Ruth_43"   "Olga_1"    "Olga_2"    "Olga_4"   
##  [85] "Olga_6"    "Olga_11"   "Olga_13"   "Olga_14"   "Olga_15"   "Olga_16"  
##  [91] "Olga_18"   "Olga_19"   "Olga_20"   "Olga_21"   "Olga_22"   "Olga_23"  
##  [97] "Olga_24"   "Olga_25"   "Olga_26"   "Olga_27"   "Olga_28"   "Olga_29"  
## [103] "Olga_31"   "Olga_33"   "Olga_34"   "Olga_35"   "Olga_36"   "Olga_37"  
## [109] "Olga_38"   "Olga_40"   "Olga_41"   "Olga_42"   "Olga_43"
for (u in 1:length(unique(trips$tripIDnight)))
{
  wildboar_trip <- trips %>% filter(tripIDnight==unique(trips$tripIDnight)[u])
  
  # Only take full trips
  if (length(wildboar_trip$TierID) > 61)
  {
    # Get wildboard schreck assosiated to wild boar
    schreck <- schreck_locations_ch %>%  filter(id == max(wildboar_trip$closest_schreck))
    # Calculate distance and time differences
    wildboar_trip <- wildboar_trip %>% 
      mutate(dist = sqrt((first(schreck$N)-N)^2+(first(schreck$E)-E)^2),
        triptime0 = as.numeric((DatetimeUTC - min(DatetimeUTC))) / 60,
        triptimeDiff = (triptime0-lag(triptime0))*60,
        distanceAbsolute = ((E- lag(E))^2 + (N-lag(N))^2)^0.5) %>% arrange(.,triptime0)
    
    # Calculate approaching rate
    wildboar_trip <- wildboar_trip %>%  
      mutate(approachingRate = lag(dist)-dist,
             approachingRateAbsolute = approachingRate / (triptimeDiff),
             approachingRateRelative = approachingRate / (distanceAbsolute),
             approachingRateRelative = ifelse(is.na(approachingRateRelative), 0, approachingRateRelative))
    # Calculate speed & acceleration
    wildboar_trip <- wildboar_trip %>%
      mutate(speed = round(distanceAbsolute / triptimeDiff, 4),
             acceleration = round((lead(speed)-speed)  * 60 / lead(triptimeDiff), 4),
             speedDiff3 = round(((speed+lead(speed)+lead(speed, 2))/3 - 
                                      (lag(speed)+lag(speed, 2)+lag(speed, 3)/3)), 4))

    # Relative coordinates (trajectory)
    wildboar_trip <- wildboar_trip %>% mutate(x=E-first(s1$E), y=N-first(s1$N))
    coords <- data.frame(x = wildboar_trip$x, 
                         y = wildboar_trip$y, 
                         times = wildboar_trip$triptime0)
    # Set all sinousitiy to 0
    wildboar_trip$sinousity = replicate(length(coords$x), NA)
    for (i in 1:(length(coords$x)-3)) {
      # Create a trajectory from the coordinates
      trj <- TrajFromCoords(coords[(i):(i+3),])
      # Rescale stepsize
      trj_re <- TrajRediscretize(trj, 1)
      # Calculate sinousity
      wildboar_trip$sinousity[i] = TrajSinuosity(trj_re, compass.direction = TRUE) %>% 
        round(digits = 4)
    }
    
    # Get proximity of movement
    wildboar_trip <- wildboar_trip %>%
     mutate(linedist = ((x^2+y^2)^0.5 + ((x-lag(x))^2+(y-lag(y))^2)^0.5)/2)
    # Get relative direction to scare device
    wildboar_trip <- wildboar_trip %>% mutate(directionRelative=
                                                ifelse(abs(x) > abs(y),
                                                       ifelse(x>0, "E", "W"),
                                                       ifelse(y>0, "N", "S")))
    # Select
    wildboar_trip_scared <- wildboar_trip_scared %>% 
      rbind(wildboar_trip %>% 
              dplyr::select(id, TierName, triptime0, approachingRate, approachingRateRelative,
                            approachingRateAbsolute, speed, sinousity, linedist, acceleration, 
                            speedDiff3, dist, E, N, x, y, directionRelative, closest_schreck, tripIDnight, 
                            DatetimeUTC, hour, day))
  }
}
head(wildboar_trip_scared)
## # A tibble: 6 x 22
## # Groups:   tripIDnight [1]
##      id TierName triptime0 approachingRate approachingRateRe… approachingRateAb…
##   <dbl> <chr>        <dbl>           <dbl>              <dbl>              <dbl>
## 1 15062 Ueli           0            NA                  0              NA       
## 2 15063 Ueli          15.1           3.68               0.717           0.00407 
## 3 15064 Ueli          30.0           3.38               0.488           0.00376 
## 4 15065 Ueli          45.1           0.606              0.111           0.000670
## 5 15066 Ueli          60.6          -6.72              -0.901          -0.00726 
## 6 15067 Ueli          75.1           9.57               0.869           0.0110  
## # … with 16 more variables: speed <dbl>, sinousity <dbl>, linedist <dbl>,
## #   acceleration <dbl>, speedDiff3 <dbl>, dist <dbl>, E <dbl>, N <dbl>,
## #   x <dbl>, y <dbl>, directionRelative <chr>, closest_schreck <chr>,
## #   tripIDnight <chr>, DatetimeUTC <dttm>, hour <int>, day <chr>
# Scaled values
wildboar_trip_scared <- wildboar_trip_scared %>% group_by(tripIDnight) %>% 
  mutate(
    approachingRateRelativeS = (approachingRateRelative-min(approachingRateRelative, na.rm = TRUE)) /
      (max(approachingRateRelative, na.rm = TRUE)- min(approachingRateRelative, na.rm = TRUE)),
    accelerationS = (acceleration-min(acceleration, na.rm = TRUE))/
                     (max(acceleration, na.rm = TRUE) - min(acceleration, na.rm = TRUE)),
    sinousityS = (sinousity-min(sinousity, na.rm = TRUE))/
                     (max(sinousity, na.rm = TRUE)- min(sinousity, na.rm = TRUE)),
    distanceS = (dist-min(dist, na.rm = TRUE)) / (max(dist, na.rm = TRUE) - min(dist, na.rm = TRUE)),
    speedDiff3S = (speedDiff3-min(speedDiff3, na.rm=TRUE)) / 
      (max(speedDiff3, na.rm=TRUE)-min(speedDiff3, na.rm = TRUE)),
    approachingRateRelativeSlead = lead(approachingRateRelativeS),
    scareEffect = accelerationS - sinousityS - distanceS - lead(approachingRateRelativeS) + speedDiff3S,
    scareEffectShow = ifelse(scareEffect < 0, -0.1, scareEffect),
    scary= scareEffect > 1.4,
    scary= ifelse(is.na(scary), FALSE, scary),
    scary= ifelse(scary&lag(scary), FALSE, scary)) %>% 
  ungroup()

# Get scared points
wildboar_scared <- wildboar_trip_scared %>% filter(scary)

# Get scared trips
wildboar_trip_scared_True <- wildboar_trip_scared %>% 
  group_by(tripIDnight) %>% 
  mutate(tripScared = sum(scary, na.rm = TRUE)) %>% 
  filter(tripScared > 0)

# Join trips with scared wild boars
w <- w %>% left_join(wildboar_trip_scared_True %>% ungroup() %>% 
                       dplyr::select(id, scareEffect, triptime0, accelerationS,
                                     approachingRateRelativeSlead, speedDiff3S, sinousityS,
                                     distanceS, x, y, scary), by="id")

Plot all found shrecks

# Amount of scared trips
unique(wildboar_trip_scared$tripIDnight)
##  [1] "Ueli_110"  "Ueli_114"  "Ueli_119"  "Ueli_124"  "Ueli_127"  "Ueli_133" 
##  [7] "Ueli_137"  "Sabine_2"  "Sabine_18" "Sabine_20" "Sabine_21" "Sabine_22"
## [13] "Sabine_23" "Sabine_24" "Sabine_25" "Sabine_26" "Sabine_27" "Sabine_28"
## [19] "Sabine_29" "Sabine_30" "Sabine_31" "Sabine_32" "Sabine_33" "Sabine_34"
## [25] "Sabine_35" "Sabine_36" "Sabine_37" "Sabine_38" "Sabine_39" "Sabine_40"
## [31] "Sabine_41" "Sabine_42" "Sabine_43" "Ruth_12"   "Ruth_17"   "Ruth_40"  
## [37] "Ruth_42"   "Ruth_43"   "Olga_1"    "Olga_2"    "Olga_4"    "Olga_6"   
## [43] "Olga_11"   "Olga_13"   "Olga_14"   "Olga_15"   "Olga_16"   "Olga_18"  
## [49] "Olga_19"   "Olga_20"   "Olga_21"   "Olga_22"   "Olga_23"   "Olga_24"  
## [55] "Olga_25"   "Olga_26"   "Olga_27"   "Olga_28"   "Olga_29"   "Olga_31"  
## [61] "Olga_33"   "Olga_34"   "Olga_35"   "Olga_36"   "Olga_37"   "Olga_38"  
## [67] "Olga_40"   "Olga_41"   "Olga_42"   "Olga_43"
unique(wildboar_trip_scared_True$tripIDnight)
##  [1] "Sabine_21" "Sabine_23" "Sabine_24" "Sabine_28" "Sabine_31" "Sabine_35"
##  [7] "Sabine_36" "Sabine_39" "Sabine_40" "Sabine_43" "Ruth_40"   "Ruth_43"  
## [13] "Olga_4"    "Olga_16"   "Olga_19"   "Olga_24"   "Olga_27"   "Olga_28"  
## [19] "Olga_31"   "Olga_33"   "Olga_34"   "Olga_35"   "Olga_36"   "Olga_43"
for (u in 1:length(unique(wildboar_trip_scared_True$tripIDnight))) 
{
  test <- wildboar_trip_scared_True %>% filter(tripIDnight==unique(wildboar_trip_scared_True$tripIDnight)[u])
  print(first(test$DatetimeUTC))
  print(first(test$tripIDnight))
  # Variable values
  print(ggplot(data=test) + 
    geom_bar(stat="identity", aes(x=triptime0/60, y=scareEffectShow),fill = "grey", size=1.3)+ 
    geom_line( aes(x=triptime0/60, y=distanceS, alpha=0.8), color="blue", size=1.3) +
    geom_line( aes(x=triptime0/60, y=approachingRateRelativeSlead, alpha=0.8), color="green", size=1.3) +
    geom_line( aes(x=triptime0/60, y=speedDiff3S, alpha=0.8), color="red", size=1.3) +
    geom_line( aes(x=triptime0/60, y=sinousityS, alpha=0.8), color="turquoise", size=1.3) +
    geom_line( aes(x=triptime0/60, y=accelerationS, alpha=0.8), color="orange", size=1.3) +
    ggtitle(paste("Wildboar trip", first(test$tripIDnight), "started at", first(test$DatetimeUTC))) +
    xlab("Hour passed by from 18:00 p.m.") + ylab("Scaled variables (0 to 1) and scare effect (-3 to 2)"))
  
    testClose <- test %>% 
      filter(scary | lead(scary) | lead(scary,2)| lead(scary,3)| lead(scary,4) | lag(scary,1)| lag(scary,2)| lag(scary,3)| lag(scary,4))
  print(first(test$DatetimeUTC))
  print(first(test$tripIDnight))
  print(ggplot(data=testClose) + 
    geom_bar(stat="identity", aes(x=(triptime0-testClose$triptime0[5]), y=scareEffectShow),
             fill = "grey", size=1.3)+
    geom_line(aes(x=(triptime0-testClose$triptime0[5]), y=distanceS, alpha=0.8, colour="Distance"),
               color="blue", size=1.3) +
    geom_line( aes(x=(triptime0-testClose$triptime0[5]), 
                   y=approachingRateRelativeSlead, alpha=0.8), color="green", size=1.3) +
    geom_line( aes(x=(triptime0-testClose$triptime0[5]), y=accelerationS, alpha=0.8), color="orange", size=1.3) +
    geom_line( aes(x=(triptime0-testClose$triptime0[5]), y=speedDiff3S, alpha=0.8), color="red", size=1.3) +
    geom_line( aes(x=(triptime0-testClose$triptime0[5]), y=sinousityS, alpha=0.8), color="turquoise", size=1.3) +
    ggtitle(paste("Wildboar trip", first(testClose$tripIDnight), "1 hour before and after scare effect at",
                  testClose$DatetimeUTC[5])) +
    xlab("Minutes before or after the scare effect") + 
    ylab("Scaled variables (0 to 1) and scare effect (-3 to 2)")+ 
    theme(legend.position = "bottom"))
  
  print(ggplot() +
    geom_path(data= testClose, aes(x=E, y=N, color=(triptime0-testClose$triptime0[5]))) +
    geom_point(data = schreck_locations_ch %>% filter(id == max(test$closest_schreck)), 
               aes(x=E, y=N), colour="black", size=2) +
    geom_point(data = test %>% filter(scary), aes(x=E, y=N), color="red")+
    ggtitle(paste("Wildboar trip", first(test$tripIDnight), "started at", first(test$DatetimeUTC))))
  
  print(ggplot() +
    geom_path(data= test, aes(x=E, y=N, color=triptime0)) +
    geom_point(data = schreck_locations_ch %>% filter(id == max(test$closest_schreck)), 
               aes(x=E, y=N), colour="black", size=2) +
    geom_point(data = test %>% filter(scary), aes(x=E, y=N), color="red")+
    ggtitle(paste("Wildboar trip", first(testClose$tripIDnight), "1 hour before and after scare effect at",
                  testClose$DatetimeUTC[5])))
}
## [1] "2015-06-08 18:01:16 UTC"
## [1] "Sabine_21"

## [1] "2015-06-08 18:01:16 UTC"
## [1] "Sabine_21"

## [1] "2015-06-10 18:00:20 UTC"
## [1] "Sabine_23"

## [1] "2015-06-10 18:00:20 UTC"
## [1] "Sabine_23"

## [1] "2015-06-11 18:00:12 UTC"
## [1] "Sabine_24"

## [1] "2015-06-11 18:00:12 UTC"
## [1] "Sabine_24"

## [1] "2015-06-15 18:00:22 UTC"
## [1] "Sabine_28"

## [1] "2015-06-15 18:00:22 UTC"
## [1] "Sabine_28"

## [1] "2015-06-18 18:00:12 UTC"
## [1] "Sabine_31"

## [1] "2015-06-18 18:00:12 UTC"
## [1] "Sabine_31"

## [1] "2015-06-22 18:00:44 UTC"
## [1] "Sabine_35"

## [1] "2015-06-22 18:00:44 UTC"
## [1] "Sabine_35"

## [1] "2015-06-23 18:00:08 UTC"
## [1] "Sabine_36"

## [1] "2015-06-23 18:00:08 UTC"
## [1] "Sabine_36"

## [1] "2015-06-26 18:00:15 UTC"
## [1] "Sabine_39"

## [1] "2015-06-26 18:00:15 UTC"
## [1] "Sabine_39"

## [1] "2015-06-27 18:00:10 UTC"
## [1] "Sabine_40"

## [1] "2015-06-27 18:00:10 UTC"
## [1] "Sabine_40"

## [1] "2015-06-30 18:01:08 UTC"
## [1] "Sabine_43"

## [1] "2015-06-30 18:01:08 UTC"
## [1] "Sabine_43"

## [1] "2015-06-27 18:02:34 UTC"
## [1] "Ruth_40"

## [1] "2015-06-27 18:02:34 UTC"
## [1] "Ruth_40"

## [1] "2015-06-30 18:00:43 UTC"
## [1] "Ruth_43"

## [1] "2015-06-30 18:00:43 UTC"
## [1] "Ruth_43"

## [1] "2015-05-22 18:00:15 UTC"
## [1] "Olga_4"

## [1] "2015-05-22 18:00:15 UTC"
## [1] "Olga_4"

## [1] "2015-06-03 18:00:16 UTC"
## [1] "Olga_16"

## [1] "2015-06-03 18:00:16 UTC"
## [1] "Olga_16"

## [1] "2015-06-06 18:00:11 UTC"
## [1] "Olga_19"

## [1] "2015-06-06 18:00:11 UTC"
## [1] "Olga_19"

## [1] "2015-06-11 18:00:20 UTC"
## [1] "Olga_24"

## [1] "2015-06-11 18:00:20 UTC"
## [1] "Olga_24"

## [1] "2015-06-14 18:00:38 UTC"
## [1] "Olga_27"

## [1] "2015-06-14 18:00:38 UTC"
## [1] "Olga_27"

## [1] "2015-06-15 18:00:21 UTC"
## [1] "Olga_28"

## [1] "2015-06-15 18:00:21 UTC"
## [1] "Olga_28"

## [1] "2015-06-18 18:00:10 UTC"
## [1] "Olga_31"

## [1] "2015-06-18 18:00:10 UTC"
## [1] "Olga_31"

## [1] "2015-06-20 18:00:13 UTC"
## [1] "Olga_33"

## [1] "2015-06-20 18:00:13 UTC"
## [1] "Olga_33"

## [1] "2015-06-21 18:00:09 UTC"
## [1] "Olga_34"

## [1] "2015-06-21 18:00:09 UTC"
## [1] "Olga_34"

## [1] "2015-06-22 18:00:10 UTC"
## [1] "Olga_35"

## [1] "2015-06-22 18:00:10 UTC"
## [1] "Olga_35"

## [1] "2015-06-23 18:00:12 UTC"
## [1] "Olga_36"

## [1] "2015-06-23 18:00:12 UTC"
## [1] "Olga_36"

## [1] "2015-06-30 18:00:37 UTC"
## [1] "Olga_43"

## [1] "2015-06-30 18:00:37 UTC"
## [1] "Olga_43"

Calculate noise exposition at scared data points and for trips.

wildboar_scared_schreck <- wildboar_scared %>% 
  left_join(schreck_locations_ch, by=c("closest_schreck"="id"))  %>% 
  filter(datum_on < DatetimeUTC & datum_off > DatetimeUTC)

wildboar_scared_schreck <- wildboar_scared_schreck %>% 
  select(tripIDnight, DatetimeUTC, ausrichtung_dir, directionRelative, dist, lautstaerke, scareEffect, modus, day) %>% 
  mutate(sameDirection=ausrichtung_dir==directionRelative,
         #soundDistance=lautstaerke/dist,
         #schreckProbabily= soundDistance*(1+sameDirection)/ (50/50*2) * 100,
         volumeDir = ifelse(sameDirection, lautstaerke, 
                           ifelse(ausrichtung_dir=='S'&directionRelative=='N' | 
                                    ausrichtung_dir=='N'& directionRelative=='S' | 
                                    ausrichtung_dir=='W'& directionRelative=='E'| 
                                    ausrichtung_dir=='E'& directionRelative=='W', 
                                  lautstaerke-10, lautstaerke-5)),
         soundDistance = volumeDir - abs(20 * log(1/(1.581*dist), 10)),
         soundDistance = ifelse(day=="Tag", -20, soundDistance),
         schreckProbabily = soundDistance *100/40)

#plot(wildboar_scared_schreck$scareEffect, wildboar_scared_schreck$schreckProbabily)
plot(wildboar_scared_schreck$scareEffect, wildboar_scared_schreck$soundDistance)

#wildboar_scared_schreck2 <- wildboar_scared_schreck %>% filter(scareEffect > 1.40)
wildboar_trips <- wildboar_trip_scared %>% group_by(tripIDnight) %>% 
  filter(scareEffect == max(scareEffect, na.rm=TRUE)) %>% ungroup() %>% 
  left_join(schreck_locations_ch, by=c("closest_schreck"="id"))  %>% 
  filter(datum_on < DatetimeUTC & datum_off > DatetimeUTC)
wildboar_trips <- wildboar_trips %>% 
  select(id, tripIDnight, TierName, DatetimeUTC, scary, ausrichtung_dir, directionRelative, 
         dist, lautstaerke, scareEffect, modus, day) %>% 
  mutate(sameDirection=ausrichtung_dir==directionRelative,
         #soundDistance=lautstaerke/dist,
         #schreckProbabily= soundDistance*(1+sameDirection)/ (50/50*2) * 100,
         volumeDir = ifelse(sameDirection, lautstaerke, 
                           ifelse(ausrichtung_dir=='S' & directionRelative=='N' | 
                                    ausrichtung_dir=='N' & directionRelative=='S' | 
                                    ausrichtung_dir=='W' & directionRelative=='E'| 
                                    ausrichtung_dir=='E' & directionRelative=='W', 
                                  lautstaerke-10, lautstaerke-5)),
         soundDistance = volumeDir - abs(20 * log(1/(1.581*dist), 10)),
         soundDistance = ifelse(day=="Tag", -20, soundDistance),
        scareSound= ifelse(soundDistance < 10, FALSE, scary))

wildboar_trips$sum <- replicate(length(wildboar_trips$TierName), 1)
wildboar_scared <- wildboar_scared %>% 
  left_join(wildboar_trips %>% select(id, soundDistance, scareSound), by=c("id"="id"))
wildboar_scared <- wildboar_scared %>% filter(scareSound)

wildboar_trips2 <- wildboar_trip_scared  %>% 
  left_join(schreck_locations_ch, by=c("closest_schreck"="id"))  %>% 
  filter(datum_on < DatetimeUTC & datum_off > DatetimeUTC) %>% 
  select(tripIDnight, TierName, DatetimeUTC, scary, ausrichtung_dir, directionRelative, 
         dist, lautstaerke, scareEffect, modus, day) %>% 
  mutate(sameDirection=ausrichtung_dir==directionRelative,
         #soundDistance=lautstaerke/dist,
         #schreckProbabily= soundDistance*(1+sameDirection)/ (50/50*2) * 100,
         volumeDir = ifelse(sameDirection, lautstaerke, 
                           ifelse(ausrichtung_dir=='S' & directionRelative=='N' | 
                                    ausrichtung_dir=='N' & directionRelative=='S' | 
                                    ausrichtung_dir=='W' & directionRelative=='E'| 
                                    ausrichtung_dir=='E' & directionRelative=='W', 
                                  lautstaerke-10, lautstaerke-5)),
         soundDistance = volumeDir - abs(20 * log(1/(1.581*dist), 10)),
         soundDistance = ifelse(day=="Tag", -20, soundDistance),
         schreckProbabily = soundDistance *100/40) 
wildboar_trips2 <- wildboar_trips2 %>% group_by(tripIDnight) %>% 
  filter(soundDistance == max(soundDistance, na.rm=TRUE)) %>% ungroup() 
wildboar_trips2$sum <- replicate(length(wildboar_trips2$TierName), 1)


plot(wildboar_trips2$scareEffect, wildboar_trips2$soundDistance)

ggplot(data=wildboar_trips) + 
    geom_bar(stat="identity", aes(x=TierName, y=sum),
             fill = "grey", size=1.3) +
    geom_bar(data = wildboar_trips2 %>% filter(soundDistance > 20), stat="identity", aes(x=TierName, y= sum),
             fill = "#ff9999", size=1.3) +
    geom_bar(data = wildboar_trips %>% filter(scary), stat="identity", aes(x=TierName, y=sum),
             fill = "#e83c3c", size=1.3) +
  geom_bar(data = wildboar_trips %>% filter(soundDistance > 10 & scary), stat="identity", aes(x=TierName, y= sum),
             fill = "darkred", size=1.3) +
    xlab("Wild boar animal") + 
    ylab("Amount of trips")

wildboar_trips %>% filter(soundDistance > 20 & scary)
## # A tibble: 13 x 17
##        id tripIDnight TierName DatetimeUTC         scary ausrichtung_dir
##     <dbl> <chr>       <chr>    <dttm>              <lgl> <chr>          
##  1  42304 Sabine_24   Sabine   2015-06-12 02:00:43 TRUE  S              
##  2  42689 Sabine_28   Sabine   2015-06-16 02:15:09 TRUE  S              
##  3  42970 Sabine_31   Sabine   2015-06-19 00:30:13 TRUE  S              
##  4  43745 Sabine_39   Sabine   2015-06-27 02:15:11 TRUE  S              
##  5  43838 Sabine_40   Sabine   2015-06-28 01:30:10 TRUE  S              
##  6 193706 Olga_24     Olga     2015-06-12 02:15:17 TRUE  S              
##  7 193979 Olga_27     Olga     2015-06-14 22:45:13 TRUE  S              
##  8 194085 Olga_28     Olga     2015-06-16 01:15:09 TRUE  S              
##  9 194373 Olga_31     Olga     2015-06-19 01:30:10 TRUE  S              
## 10 194558 Olga_33     Olga     2015-06-20 23:45:12 TRUE  S              
## 11 194664 Olga_34     Olga     2015-06-22 02:15:12 TRUE  S              
## 12 194768 Olga_35     Olga     2015-06-23 04:15:14 TRUE  S              
## 13 194845 Olga_36     Olga     2015-06-23 23:30:11 TRUE  S              
## # … with 11 more variables: directionRelative <chr>, dist <dbl>,
## #   lautstaerke <dbl>, scareEffect <dbl>, modus <chr>, day <chr>,
## #   sameDirection <lgl>, volumeDir <dbl>, soundDistance <dbl>,
## #   scareSound <lgl>, sum <dbl>
schreck_locations_ch
## Simple feature collection with 25 features and 22 fields
## Geometry type: POINT
## Dimension:     XY
## Bounding box:  xmin: 2549490 ymin: 1184326 xmax: 2582053 ymax: 1214438
## Projected CRS: CH1903+ / LV95
## # A tibble: 25 x 23
##    id     region flurname  kultur   installationsho… zaun  jagddruck   lat   lon
##  * <chr>  <chr>  <chr>     <chr>               <dbl> <chr> <chr>     <dbl> <dbl>
##  1 WSS_2… fanel  tannenhof kartoff…              1.8 ja    gering     47.0  7.06
##  2 WSS_2… fanel  tannenhof karotte…              1.8 nein  gering     47.0  7.06
##  3 WSS_2… fanel  fanelach… kartoff…              1.8 nein  gering     47.0  7.04
##  4 WSS_2… fanel  fanelach… kartoff…              1.8 nein  gering     47.0  7.04
##  5 WSS_2… fanel  tannenhof weizen                1.8 nein  gering     47.0  7.06
##  6 WSS_2… fanel  tannenhof weizen                1.8 nein  gering     47.0  7.06
##  7 WSS_2… fanel  fanelach… weizen                1.8 nein  gering     47.0  7.04
##  8 WSS_2… fanel  fanelach… weizen                1.8 nein  gering     47.0  7.04
##  9 WSS_2… fanel  gampelen… weizen&…              2   nein  gering     47.0  7.05
## 10 WSS_2… fanel  gampelen… weizen&…              2   nein  gering     47.0  7.05
## # … with 15 more rows, and 14 more variables: geometry <POINT [m]>, N <dbl>,
## #   E <dbl>, datum_on <dttm>, datum_off <dttm>, modus <chr>, lautstaerke <dbl>,
## #   intervall <dbl>, ausrichtung_min <int>, ausrichtung_max <int>, phase <dbl>,
## #   wid <chr>, ausrichtung_mean <dbl>, ausrichtung_dir <chr>

How long does the scare-off effect last

w$return <- replicate(length(w$N), "NA")
w$return <- as.character(w$return)
w$return_hrs <- NA

for(j in 1:nrow(trip_list)){
  w$return <- as.character(w$return)
  w$return_hrs <- as.numeric(as.character(w$return_hrs))
  under400 <- w[w$TierName==trip_list$TierName[j] & w$DatetimeUTC>trip_list$DatetimeUTC[j] + 4*60*60 & w$closest_schreck==trip_list$closest_schreck[j] & !is.na(w$distance_to_closest_schreck),]

under400 <- under400[order(under400$DatetimeUTC),]
 if(nrow(under400)==0){w[w$tripIDnight==trip_list$tripIDnight[j] & !is.na(w$tripIDnight),]$return <- "never"
 w[w$tripIDnight==trip_list$tripIDnight[j] & !is.na(w$tripIDnight), "return_hrs"] <- NA
 } else{
   w[w$tripIDnight==trip_list$tripIDnight[j] & !is.na(w$tripIDnight), "return"] <- as.character(under400[1,]$DatetimeUTC)
   w$return <- as.POSIXct(w$return, format="%Y-%m-%d %H:%M:%S",tz="UTC")
   return_time <- as.numeric(as.character(difftime(under400[1,]$DatetimeUTC,trip_list[j,]$DatetimeUTC, units="hours")))
   w[w$tripIDnight==trip_list$tripIDnight[j] & !is.na(w$tripIDnight),"return_hrs"] <- return_time
 }}

w$return_hrs <- round(w$return_hrs,digits = 2)

### file with trips
tt<-w[!is.na(w$tripIDnight) & w$isNearestPoint=="yes",]
head(data.frame(tt))
##   TierID TierName CollarID         DatetimeUTC       E       N           day
## 1      1     Ueli    12272 2014-05-29 01:00:29 2570591 1205129 2Nachtviertel
## 2      1     Ueli    12272 2014-05-30 01:00:15 2570746 1204849 2Nachtviertel
## 3      1     Ueli    12272 2014-05-31 02:45:12 2570808 1204667 3Nachtviertel
## 4      1     Ueli    12272 2014-06-01 02:30:08 2570881 1204652 3Nachtviertel
## 5      1     Ueli    12272 2014-06-02 02:30:09 2570976 1204687 3Nachtviertel
## 6      1     Ueli    12272 2014-06-03 01:30:11 2570976 1204687 3Nachtviertel
##   moonilumination       date     time hour  id closest_schreck
## 1     0.002343097 2014-05-29 01:00:29    1  17     WSS_2014_04
## 2     0.023774932 2014-05-30 01:00:15    1  69     WSS_2014_05
## 3     0.066684515 2014-05-31 02:45:12    2 128     WSS_2014_05
## 4     0.129136625 2014-06-01 02:30:08    2 179     WSS_2014_05
## 5     0.208314672 2014-06-02 02:30:09    2 231     WSS_2014_05
## 6     0.300647726 2014-06-03 01:30:11    1 279     WSS_2014_05
##   distance_to_closest_schreck tripdate  tripdate2 tripID_dist start_dist
## 1                    350.7307    16218 2014-05-28      Ueli_1        yes
## 2                    361.1836    16219 2014-05-29      Ueli_2        yes
## 3                    364.5964    16220 2014-05-30      Ueli_3        yes
## 4                    318.3504    16221 2014-05-31      Ueli_4        yes
## 5                    230.2212    16222 2014-06-01      Ueli_5        yes
## 6                    230.8985    16223 2014-06-02      Ueli_6        yes
##   isNearestPoint isTrip tripIDnight scareEffect triptime0 accelerationS
## 1            yes   TRUE      Ueli_1          NA        NA            NA
## 2            yes   TRUE      Ueli_2          NA        NA            NA
## 3            yes   TRUE      Ueli_3          NA        NA            NA
## 4            yes   TRUE      Ueli_4          NA        NA            NA
## 5            yes   TRUE      Ueli_5          NA        NA            NA
## 6            yes   TRUE      Ueli_6          NA        NA            NA
##   approachingRateRelativeSlead speedDiff3S sinousityS distanceS  x  y scary
## 1                           NA          NA         NA        NA NA NA    NA
## 2                           NA          NA         NA        NA NA NA    NA
## 3                           NA          NA         NA        NA NA NA    NA
## 4                           NA          NA         NA        NA NA NA    NA
## 5                           NA          NA         NA        NA NA NA    NA
## 6                           NA          NA         NA        NA NA NA    NA
##                return return_hrs
## 1 2014-06-02 00:45:19      95.75
## 2 2014-05-31 02:45:12      25.75
## 3 2014-06-01 01:30:16      22.75
## 4 2014-06-02 01:01:08      22.52
## 5 2014-06-03 00:30:10      22.00
## 6 2014-06-03 22:02:12      20.53
 ph<-ggplot(tt,aes(x=return_hrs))+geom_histogram(binwidth = 6)+theme_bw()+xlab("hours until animal returned within 400 of schreck [m]")
 ph
## Warning: Removed 7 rows containing non-finite values (stat_bin).

#### 
 meanreturn<-aggregate(return_hrs~TierName, data=tt,mean)
 nreturn<-aggregate(return_hrs~TierName, data=tt,length)
 sdreturn<-aggregate(return_hrs~TierName, data=tt,sd)
 
 ## calculate standard error from sample size (n) and standard diviation (sd)
 meanreturn$SE<-sdreturn$return_hrs/sqrt(sdreturn$return_hrs)
meanreturn$lwr<-meanreturn$return_hrs-meanreturn$SE
meanreturn$upr<-meanreturn$return_hrs+meanreturn$SE

 pm<-ggplot(meanreturn,aes(y=return_hrs,x=TierName))+geom_point()
 pm<-pm+geom_errorbar(data=data.frame(meanreturn),aes(ymin=lwr,ymax=upr),width=0.4)+theme_bw()+xlab("Animal ID")+ylab("mean and SE of hours until return")
 
 ## boxplot
 ptt<-ggplot(tt,aes(y=return_hrs,x=TierName))+geom_boxplot()+theme_bw()
 ptt
## Warning: Removed 7 rows containing non-finite values (stat_boxplot).

 ### correlation
 ttt<-tt[!is.na(tt$scareEffect),]
 ### table(ttt$TierName) very few trips, majority by Olga (19) and Sabine (16)
 plot(ttt$scareEffect,ttt$return_hrs)
 abline(lm(ttt$return_hrs~ttt$scareEffect),col="red")

 cor.test(ttt$scareEffect,ttt$return_hrs, method="spearman")
## Warning in cor.test.default(ttt$scareEffect, ttt$return_hrs, method =
## "spearman"): Cannot compute exact p-value with ties
## 
##  Spearman's rank correlation rho
## 
## data:  ttt$scareEffect and ttt$return_hrs
## S = 1369.7, p-value = 0.6332
## alternative hypothesis: true rho is not equal to 0
## sample estimates:
##       rho 
## 0.1106053
 ## linear model
 mp<-ggplot(ttt,aes(y=return_hrs,x=scareEffect))+geom_point()+geom_smooth(method="lm")+theme_bw()+ylab("hours until return")
 mp
## `geom_smooth()` using formula 'y ~ x'
## Warning: Removed 3 rows containing non-finite values (stat_smooth).
## Warning: Removed 3 rows containing missing values (geom_point).

m<-lm(return_hrs~scareEffect,data=ttt) ## model assumptions not very well fulfilled, see qqplot
par(mfrow=c(2,2))
plot(m)

par(mfrow=c(1,1))
summary(m)     
## 
## Call:
## lm(formula = return_hrs ~ scareEffect, data = ttt)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -18.5118 -15.4839   0.3923   2.7156  26.7037 
## 
## Coefficients:
##             Estimate Std. Error t value Pr(>|t|)    
## (Intercept)   20.051      4.808   4.171 0.000519 ***
## scareEffect    2.519      5.952   0.423 0.676865    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 14.93 on 19 degrees of freedom
##   (3 Beobachtungen als fehlend gelöscht)
## Multiple R-squared:  0.00934,    Adjusted R-squared:  -0.0428 
## F-statistic: 0.1791 on 1 and 19 DF,  p-value: 0.6769

Adding Crop Field

#crop <- read_sf("Feldaufnahmen_Fanel.gpkg")